Investigating the epidemiological and economic effects of a third-party certification policy for restaurants with COVID-19 prevention measures

This study investigates the effects of a third-party certification policy for restaurants (including bars) that comply with indoor infection-prevention measures on COVID-19 cases and economic activities. We focus on the case of Yamanashi Prefecture in Japan, which introduced a third-party certification policy that accredits facilities, predominantly restaurants, that comply with the designated guidelines. We employ a difference-in-differences design for each of our epidemiological and economic analyses. The estimation results show that, from July 2020 to April 2021, the certification policy reduced the total number of new infection cases by approximately 45.3% (848 cases), while increasing total sales and the number of customers per restaurant by approximately 12.8% (3.21 million Japanese yen or $30,000) and 30.3% (2909 customers), respectively, compared to the non-intervention scenarios. The results suggest that a third-party certification policy can be an effective policy to mitigate the trade-off between economic activities and infection prevention during a pandemic, especially when effective vaccines are not widely available.


A.1 COVID-19 policy dummy variables
For the emergency declaration dummy variable, we use data on the progress of the government's response as summarized by Tottori Prefecture on its website for new COVID-19 infections. The site lists the prefectures that are subject to the issuance, change, and cancellation of emergency declarations in chronological order. Based on this information, we determined whether each prefecture was under a state of emergency declaration at a certain time. For the school closure dummy variable, we use data on information of school closures for the national government and each prefecture in the time-series news archives on NHK's special website for COVID-19. The variable was set to be 1 only when schools are closed in the entire prefecture. For municipality or school-level closure, the variable is set to be 0. The gathering restriction dummy variable is created based on the governor's press conferences and updates on COVID-19 in each prefecture. The variable takes a value of one if there is a restriction on event capacity of 5,000 people or less and a capacity ratio of 50% or less in each prefecture, and takes a value of zero if either of these criteria is not met. In cases where the prefectural criteria are based on the guidelines of the respective industry, the dummies are created based on the common guidelines of event-related industries.

A.2 COVID-19 test cases
In the analysis of the infection prevention effect, we use the data on the number of COVID-19 test cases published by each prefecture. As of 2020, the number of tests in each prefecture varies greatly in Japan, particularly because of varying capacities for conducting tests across prefectures. If the number of tests itself is small, the number of new infection cases may be underestimated.

A.3 Weather data
For the weather data of temperature and precipitation, we use the daily weather observation data of observatories in each prefecture using the "Past Weather Data Search" of the Japan Meteorological Agency. The average rainfall and the average temperature are used as the representative values. When extracting the data from the database, several municipalities with observatories are chosen from several municipalities with the top population. In detail, Yamanashi Prefecture is represented by Kofu and Kawaguchiko; Nagano Prefecture by Nagano, Matsumoto, Ueda, and Iida; Shizuoka Prefecture by Hamamatsu, Shizuoka, and Fuji; Gunma Prefecture by Maebashi and Isesaki; Ibaraki Prefecture by Tsukuba, Mito, and Hitachi; and Tochigi Prefecture by Utsunomiya and Oyama. The weather data for each prefecture is the average of the data for the municipalities belonging to each prefecture.

A.4 Human mobility inflow and outflow by prefecture
We use Agoop's paid data for the human flow within each prefecture and the human flow from outside the prefecture into each prefecture. The data is drawn from users' GPS information held by Agoop. It is the data that calculates the entire human flow from a sample of the number of people who existed at a certain coordinate at a certain time. The data provides information on not only the total number of people in a prefecture, but also the movement of people from a specific prefecture to a specific prefecture/municipality. This data is used to estimate the number of potentially infectious mobility coming from other prefectures as shown in the susceptible-infectious-recovered (SIR) model, rather than as an objective variable.

A.5 Restaurant information views online
We also use data on the rate of increase in the number of restaurant information views online per week, which is available on V-RESAS published by the Cabinet Office of Japan. The rate of increase or decrease in the restaurant information views online compared to the same week in 2019 is disclosed for each prefecture. The original data is held by Retty Inc., which operates Japan's largest word-of-mouth gourmet service. This data is used as the dependent variable in the robustness check (D.4 Restaurant information views online)

A.6 Human mobility by facility type
For the mobility data, we also use the "COVID-19: Community Mobility Report" published by Google. The data reveals the rate of increase or decrease in human flow in six types of locations ("retail and recreation," "grocery and pharmacy," "parks," "transit stations," "workplaces," and "residential") by country and region/prefecture. The median value for each day of the week for the five-week period from January 3 to February 6, 2020 is used as the baseline for the rate of change. Thus, the daily data is the rate of change from the base values for each day of the week.This data is used as the dependent variable in the robustness check (D.5 Human mobility by facility type)

A.7 Human mobility across regions
We also used human flow data on the rate of increase or decrease in the mobility by region (intracity, intercity, and interprefectural), compared to the same week in 2019 for each prefecture. This data is available on V-RESAS. This data is used as the dependent variable in the robustness check (D.6 Inter-regional Mobility) to examine the impact on human flow in and across the prefectures.

A.8 Stay-home rate
We use the data on stay-home rate, which the Mizuno Laboratory of the National Institute of Informatics and the Graduate University for Advanced Studies publish. The data is collected by age group and time, based on the population data estimated in real-time from the information of about 78 million base stations of DOCOMO, a major Japanese telecommunication company. They define the number of people going out from residential areas as denotes infectivity, and denotes recovery or isolation rate. In other words, the rate of change of the Infectious population is dependent on the interactions between the Susceptible and Infectious populations, the infectivity of the virus, and the rate at which the Infectious move to the Removed population. We can obtain an expression for the number of new COVID-19 cases in a given time period by rewriting the ordinary differential equation as:

=
Taking logarithms on both sides, we get: A previous study has shown that the model that assumes that the proportion of susceptible people who can be infected and the proportion of infectious people who can infect others are not equal to one, but vary depending on the situation, can more accurately estimate the number of newly infected people (Law, K.B., Peariasamy, K.M., Gill, B.S. et al 2020). Therefore, we transform the model by adding coefficients to the Susceptible and Infectious variables as follows In order to adapt this basic estimation equation to changes in external circumstances that affect the infection cases, we add control variables such as economic activity variables and weather conditions. In addition, the purpose of the GZ certification policy that we want to estimate is to reduce the infectivity ( ) that can be transferred from one infected person to another. Therefore, we redefine the infectivity ( ) as follows to derive the main estimation model.  . Average rainfall(log) is the logtransformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (New infection cases(log), Cumulative GZ-certified restaurants, log, Infectious(log), and Tests(log)), we add value 1 before log-transforming to avoid the logarithm of 0. Customers per restaurant(log) is the log-transformed value of the number of customers per restaurant. Average temperature(log) is the log-transformed value of the mean temperature (Fahrenheit degrees). Average rainfall(log) is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (New infection cases(log), Cumulative GZ-certified restaurants, log, Infectious(log), and Tests(log)), we add value 1 before log-transforming to avoid the logarithm of 0. . Average rainfall(log) is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (Cumulative GZ-certified restaurants(log), and The number of new COVID-19 cases(log)), we add value 1 before log-transforming to avoid the logarithm of 0.  Average rainfall(log) is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (Cumulative GZ-certified restaurants(log), and The number of new COVID-19 cases(log)), we add value 1 before log-transforming to avoid the logarithm of 0.  Notes: *p<0.1; **p<0.05; ***p<0.01 The dependent variable is the log-transformed value of the number of new infection cases (2 week lag). The unit of analysis is prefecture and week, and the fixed effects are introduced in all models. For the observations, six prefectures are targeted, and the period of analysis is for 66 weeks from the third week of January, 2020 to the third week of April, 2021. The values in parentheses are cluster-robust standard errors. Clustering is at the prefecture level. Cumulative GZ-certified restaurants, log is the log-transformed value of the number of cumulative certified-GZ restaurants. Cumulative GZ-certified restaurants and hotels, log is the log-transformed value of the number of cumulative certified-GZ restaurants and hotels. Infectious, log is the logtransformed value of the number of potentially infected people. Susceptible, log is the log-transformed value of the total number of susceptible population. State of Emergency is the dummy variable that takes the value 1 if the state of emergency is declared. Tests (2 week lag), log is the log-transformed value of the number of COVID-19 tests. Customers per restaurant, log is the log-transformed value of the number of customers per restaurant. Average temperature, log is the log-transformed value of the mean temperature (Fahrenheit degrees). Average rainfall, log is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (New infection cases (2 week lag), log, Cumulative GZ-certified restaurants, log, Cumulative GZ-certified restaurants and hotels, log, Infectious, log, and Tests (2 week lag), log), we add value 1 before log-transforming to avoid the logarithm of 0.   Notes: *p<0.1; **p<0.05; ***p<0.01 The dependent variable is the percentage change in the number of restaurant information views online compared to the 2019 baseline (V-RESAS). The unit of analysis is prefecture-week, and the fixed effects are introduced in all models. For the observations, six prefectures are targeted, and the period of analysis is for 68 weeks from the third week of January, 2020 to the fifth week of April, 2021. The values in parentheses are cluster-robust standard errors. Clustering is at the prefecture level. Cumulative GZ-certified restaurants, log is the log-transformed value of the number of cumulative certified-GZ restaurants. State of Emergency is the dummy variable that takes the value 1 if the state of emergency is declared. The number of new COVID-19 cases, log is the log-transformed value of the daily number of infection cases. Average temperature, log is the log-transformed value of the mean temperature (Fahrenheit degrees). Average rainfall, log is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (Cumulative GZ-certified restaurants, log, and The number of new COVID-19 cases, log), we add value 1 before log-transforming to avoid the logarithm of 0.    Notes: *p<0.1; **p<0.05; ***p<0.01 The dependent variable is the percentage change in inter-regional human flow (within a city, within a prefecture, and across prefectures) compared to the 2019 baseline (V-RESAS). The unit of analysis is prefecture and week, and the fixed effects are introduced in all models. For the observations, six prefectures are targeted, and the period of analysis is for 68 weeks from the third week of January, 2020 to the fifth week of April, 2021. The values in parentheses are cluster-robust standard errors. Clustering is at the prefecture level. Cumulative GZ-certified restaurants, log is the log-transformed value of the number of cumulative certified-GZ restaurants. State of Emergency is the dummy variable that takes the value 1 if the state of emergency is declared. The number of new COVID-19 cases, log is the log-transformed value of the daily number of infection cases. Average temperature, log is the log-transformed value of the mean temperature (Fahrenheit degrees). Average rainfall, log is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (Cumulative GZ-certified restaurants, log, and The number of new COVID-19 cases, log), we add value 1 before log-transforming to avoid the logarithm of 0.

D.7 Stay-home rate
Cumulative GZ-certified restaurants, log 0.003 *  Notes: *p<0.1; **p<0.05; ***p<0.01 The dependent variable is the day-time (from 9 am to 6 pm) stayhome rate for males, which indicates the percentage of people who refrain from going out compared to the baseline value; the closer to 1, the more people refrain from going out, and the closer to 0, the more people go out. The unit of analysis is prefecture and day, and the fixed effects are introduced in all models. For the observations, six prefectures are targeted, and the period of analysis is for 449 days from January 1st, 2020 to March 24th, 2021. The values in parentheses are cluster-robust standard errors. Clustering is at the prefecture level. Cumulative GZ-certified restaurants, log is the log-transformed value of the number of cumulative certified-GZ restaurants. State of Emergency is the dummy variable that takes the value 1 if the state of emergency is declared. The number of new COVID-19 cases, log is the log-transformed value of the daily number of infection cases. Average temperature, log is the log-transformed value of the mean temperature (Fahrenheit degrees). Average rainfall, log is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (Cumulative GZ-certified restaurants, log, and The number of new COVID-19 cases, log), we add value 1 before log-transforming to avoid the logarithm of 0.   Notes: *p<0.1; **p<0.05; ***p<0.01 The dependent variable is the day-time (from 9 am to 6 pm) stayhome rate for females, which indicates the percentage of people who refrain from going out compared to the baseline value; the closer to 1, the more people refrain from going out, and the closer to 0, the more people go out. The unit of analysis is prefecture and day, and the fixed effects are introduced in all models. For the observations, six prefectures are targeted, and the period of analysis is for 449 days from January 1st, 2020 to March 24th, 2021. The values in parentheses are cluster-robust standard errors. Clustering is at the prefecture level. Cumulative GZ-certified restaurants, log is the log-transformed value of the number of cumulative certified-GZ restaurants. State of Emergency is the dummy variable that takes the value 1 if the state of emergency is declared. The number of new COVID-19 cases, log is the logtransformed value of the daily number of infection cases. Average temperature, log is the log-transformed value of the mean temperature (Fahrenheit degrees). Average rainfall, log is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (Cumulative GZ-certified restaurants, log, and The number of new COVID-19 cases, log), we add value 1 before logtransforming to avoid the logarithm of 0. Notes: *p<0.1; **p<0.05; ***p<0.01 The dependent variable is the night-time (from 8pm to 0am) stay-home rate, which indicates the percentage of people who refrain from going out compared to the baseline value; the closer to 1, the more people refrain from going out, and the closer to 0, the more people go out. The unit of analysis is prefecture and day, and the fixed effects are introduced in all models. For the observations, six prefectures are targeted, and the period of analysis is for 454 days from January 1st, 2020 to March 29th, 2021. The values in parentheses are cluster-robust standard errors. Clustering is at the prefecture level. Cumulative GZ-certified restaurants, log is the log-transformed value of the number of cumulative certified-GZ restaurants. State of Emergency is the dummy variable that takes the value 1 if the state of emergency is declared. The number of new COVID-19 cases, log is the log-transformed value of the daily number of infection cases. Average temperature, log is the log-transformed value of the mean temperature (Fahrenheit degrees). Average rainfall, log is the log-transformed value of the aggregated rainfall (in millimeters). School closure is the dummy variable that takes the value 1 if the school closure is declared. Gathering restriction is the dummy variable that takes the value 1 if the large-scale gathering restriction is declared. For the variables that take absolute value 0 (Cumulative GZ-certified restaurants, log, and The number of new COVID-19 cases, log), we add value 1 before log-transforming to avoid the logarithm of 0.

E.4.1 Prevention of infectious diseases among visitors
(1) Store entry, order, and payment • Disinfection equipment shall be installed at the entrance of the store, and hand sanitization shall be indicated at the entrance. • When there is a queue due to waiting for a turn, etc., a minimum distance of 1 meter (2 meters if no mask is worn) shall be maintained between visitors. • When serving customers face-to-face at the cash register, etc., use acrylic panels, transparent vinyl curtains, partitions, etc. to shield the customers. In addition, use coin trays or introduce cashless payment. • Those with fever (e.g., 1 degree above normal), cold symptoms (e.g., cough, sore throat), vomiting, diarrhea, etc., even if they have mild symptoms, should not be admitted. • Make it known that people should wear masks except when eating or drinking, and request that people wash their hands and disinfect their hands regularly. • Remind people to practice good cough etiquette.
• If there is an elevator, limit the number of passengers by adjusting the weight sensor of the elevator. Capacity:____, Passenger limit:___ • If there is a pick-up truck, shield the driver's seat and rear seat of the pick-up truck with an acrylic plate or transparent vinyl curtain.
(2) Meals and in-store use [One of these must be met for placement between tables] • Tables used by the same group and tables used by other groups should be placed so that there is at least 1 meter of interpersonal distance between them.
- Table-to-table distance: ___ m • Use acrylic panels, transparent plastic curtains, partitions, etc. to shield the space between tables used by the same group and tables used by other groups.
[One of the following conditions must be met for placement on the same table] Exclude cases where a small number of family members, elderly people with caregivers, infants, disabled people, etc. wish to sit face-to-face.
• Do not place seating directly in front of each other. Seating should be arranged so that the distance between seats is at least 1 meter. Seat-to-seat distance: ___m. • Install partitions on tables to shield them.
• Avoid having too many people at the same time by limiting the length of stay* and using a reservation system. (*Approximately 2 hours).
• Avoid large plates and serve food individually, or have employees serve the food.
[In buffet style, one of the following must be met] • A new small plate should be used by each user for each serving, and food and drinks should be protected by covers to prevent splashing, and masks, disposable gloves, etc. should be worn when serving. When serving, make sure to wear masks, disposable gloves, etc., and do not share tongs or chopsticks for serving. • Serve food on small plates or have staff serve food.
• Avoid setting up common tabletop condiments, pots, etc., or disinfect them when changing customers. • Remind customers not to share or use spoons, chopsticks, or other utensils. • Reduce the volume of background music in the store and remind customers to avoid loud conversations. • Coughing etiquette should be strictly observed. (For ventilation standards, see "3. Hygiene Management of Facilities and Equipment" for ventilation standards). • If the toilet has a lid, indicate that waste should be flushed after the lid is closed. • Indicate that people should wash their hands and disinfect their hands after using the restroom.
• If there is a smoking area, reduce the number of people using it at one time, and keep a distance between people. If there is a smoking area, request that the three densities be avoided by reducing the number of people using the area at once, keeping a good distance between people, etc.
-Size of the smoking space: _____ 2 Maximum capacity: _____

E.4.2 Prevention of infectious diseases among employees
• Make sure to wear masks.
• Take your temperature and check your physical condition before starting work.
• If there are multiple rooms, limit per smoking space. If you have a fever (e.g., more than 1 degree above normal), a cold (cough, sore throat, etc.), vomiting, diarrhea, or other symptoms, even if they are mild. symptoms such as vomiting or diarrhea. • Employees who are infected or suspected to be infected, or who are judged to be in close contact with infected employees, are prohibited from working. Employees who are infected, suspected to be infected, or determined to be a close contact shall not be allowed to work. • Hand disinfection and hand washing are to be performed regularly at the beginning of work, after touching areas or items that come into contact with others, after cleaning, and after using the toilet. • When accepting orders from users or serving food, be careful not to stand in front of users and maintain a safe distance from them. • In the break area, reduce the number of people taking a break at one time, and avoid eating and talking face-to-face. • Ventilate the break area at all times (for ventilation standards, refer to "3. Thorough hygiene management of facilities and equipment") and disinfect shared items on a regular basis. • Employees' uniforms should be laundered regularly after work on the day in question.

E.4.3 Thorough hygiene management of facilities and equipment
• For facilities subject to the Building Management Law*, check whether they meet the standards for air quality control based on the law, and if not, maintain and manage the ventilation equipment appropriately, including cleaning and maintenance.

- * Law Concerning the Protection of Sanitary Environments in Buildings
[For facilities not covered by the Building Management Law, one of the following must be met] • The required ventilation volume (30 m³ per hour per person) shall be secured by ventilation equipment. If the required ventilation volume is not enough, the ventilation system shall be installed. • If the required ventilation volume is not sufficient, adjust the number of people entering the store to secure the required ventilation volume per person, and properly maintain the ventilation equipment, including cleaning and maintenance. [Appeal items] This is not a mandatory requirement for certification, but it is an item that can be appealed as a voluntary effort by the business.
• The details of ventilation (air flow) in common areas where people are crowded in the facility are clearly shown. • In order to secure the required amount of ventilation per person in a densely populated common area in the facility, the ventilation system should be designed for each area.
-(In the case of limiting the number of persons to ensure the required ventilation volume) Ventilation volume: 3 /hour ÷ 30 3 /person/hour = ___person • Prohibit the use of hand dryers and common towels, and provide paper towels or encourage the use of personal towels. • Wipe down and disinfect items shared with others and areas that are touched by multiple people regularly, such as when changing users, using disinfecting ethanol, sodium hypochlorite, or commercially available detergents containing surfactants.
-[areas shared with others in the restaurant industry and frequently touched] * Tables, chairs, menu books, condiments, drink bars, doorknobs, light switches, touch panels, tabletop bells, cash registers, faucets, handrails, toilet seats, washing levers, coin trays, ticket vending machines, elevator buttons, etc.
[Appeal items] • In order to reduce the risk of contact and droplet infections, the following measures should be taken to avoid overlapping lines of flow for users. Describe in detail: ___________________________ • Those who collect garbage should wear masks and gloves, and always wash their hands after work.
• Garbage, hand towels, etc. that may have food residue, snot, saliva, etc. on them should be sealed in plastic bags. sealed in a plastic bag for disposal.

E.4.4 Preparation and publication of checklists
• Each facility or business operator shall prepare a checklist that specifies specific methods and procedures, frequency of cleaning and disinfection, spacing between people, etc., after assessing the risks in the facility, and disclose the daily checks using the checklist.

E.4.5 Policy for dealing with an outbreak of infection
• In the event that an employee of the facility is found to be infected, the facility will respond and cooperate with the public health center's instructions and investigations in a sincere and proactive manner, take measures to prevent the spread of infection from the facility, and if necessary, publicize information to prevent the spread of infection, such as business days when there is a possibility of infection. • Provide employees with training opportunities to ensure that they are taking appropriate actions to prevent the spread of infection, such as refraining from going to work if they are suspected of being infected until the test results are known. • If the results of a proactive epidemiological survey conducted by the public health center reveal that an infected person has been using the facility in question, take measures to prevent the spread of infection through the facility in question by responding and cooperating with the public health center's advice and instructions in good faith and proactively.
[Appeal Items] • In order to identify the risk of infection at an early stage, employees should be encouraged or required to use an application for notification of close contact provided by the government. • In addition to the above, introduce a system for early identification of infection risk.
Describe in detail: _______________________________________________________